A soft sensor is an inferential model that uses software techniques to estimate the value of a process quality variable using process sensor measurements. Inferential model may be built by using various data-driven methods such as: principle component regression (PCR), partial least squares (PLS), artificial neural network (ANN), support vector machines (SVM), neuro-fuzzy systems (NFS), etc.
It is well known that all those inferential models are built offline by training data which collected from processes. In certain situations, those training data cannot cover the entire input domains. If new situations arise, the building process has to be reactivated.
Techniques such as recursive PCR/PLS, adaptive PCR based on moving windows, exponential weighted PLS, and time-lagged PCR, may be used to overcome this problem. However, in all these adaptive approaches, a window of recent operating data must be memorized, and past experiences are lost as the process evolves and the model is updated.
U.S. Pat. No. 6,546,379 discloses a cascade boosting method for boosting predictive models for resolving the interpretability problem of previous boosting methods and mitigating the fragmentation problem when applied to decision trees. However, this method may cause coarse transitions from the large number of models. These coarse transitions may reduce the accuracy of the overall predictive model and may also cause confusion for the users of the overall predictive models.
U.S. Pat. No. 7,505,949 discloses a process model error correction method by integrating two models to generate the desired values of the plurality of the output parameters. This method is disadvantageous in that: it needs a lot of memory to save the data from process; and the online computational burden is large enough.
U.S. Pat. No. 6,243,696 discloses an online model to generate the predicted value for transferring to the control system. However, this algorithm also needs a historical database. On the other hand, it needs to check whether the variables' names are still valid when the system is running. If not, it is necessary to rewrite the various variables to the database.
In summarization of the foregoing description, the methods as described in the above-mentioned patents either require relatively large temporary storage space, or cannot be referenced from the required parameters in previous manufacturing processes, or cannot be used to effectively combine predicted data and actual data. Hence, there is an urgent need for a method capable of predicting output values of industrial processes and being referenced from previous and next output values by regression analysis.